class_docstr: Result of fitting a discrete or continuous distribution to data
  
  Attributes
  ----------
  params : namedtuple
      A namedtuple containing the maximum likelihood estimates of the
      shape parameters, location, and (if applicable) scale of the
      distribution.
  success : bool or None
      Whether the optimizer considered the optimization to terminate
      successfully or not.
  message : str or None
      Any status message provided by the optimizer.
code: def _cdf_plot(self, ax, fit_params):
      data = np.sort(self._data)
      ecdf = self._plotting_positions(len(self._data))
      ls = "--" if len(np.unique(data)) < 30 else "."
      xlabel = "k" if self.discrete else "x"
      ax.step(
          data, ecdf, ls, label="Empirical CDF", color="C1", zorder=0
      )
  
      xlim = ax.get_xlim()
      q = np.linspace(*xlim, 300)
      tcdf = self._dist.cdf(q, *fit_params)
  
      ax.plot(
          q, tcdf, label="Fitted Distribution CDF", color="C0", zorder=1
      )
      ax.set_xlim(xlim)
      ax.set_ylim(0, 1)
      ax.set_xlabel(xlabel)
      ax.set_ylabel("CDF")
      ax.set_title(rf"Fitted $\tt {self._dist.name}$ and Empirical CDF")
      handles, labels = ax.get_legend_handles_labels()
      ax.legend(handles[::-1], labels[::-1])
      return ax
  
imports: import warnings
  from collections import namedtuple
  import numpy
  from scipy import optimize, stats
  from scipy._lib._util import check_random_state
label_desc: Use image node_id 10 for calling the FitResult obj's underlying member method code with example usage: obj._cdf_plot(ax, fit_params) and returns: ax